Techniques For Structure Prediction Research Articles

Tetragonal and trigonal Mo2B2 monolayers: two new low-dimensional materials for Li-ion and Na-ion batteries.

In this study, we report two new Mo2B2 monolayers and investigate their stabilities, electronic structures, lattice dynamics, and properties as anode materials for energy storage by using the crystal structure prediction technique and first-principles method. The calculated phonon spectra and electrical structures indicate that our predicted tetragonal and trigonal Mo2B2 (tetr- and tri-Mo2B2) monolayers possess excellent electronic conductivity and great stability. The adsorption energies of Li/Na on them are negative enough to ensure stability and safety under operating conditions. Besides, tetr-Mo2B2 possesses a theoretical specific capacity of ∼251 mA h g-1 for both Li- and Na-ion batteries (LIBs and NIBs), while tri-Mo2B2 possesses ∼251 and ∼188 mA h g-1 for LIBs and NIBs, respectively. The diffusion energy barriers of Li/Na over tetr- (0.029/0.010 eV) and tri- (0.023/0.013 eV) Mo2B2 are very small, indicating their excellent charge/discharge capability. In addition, the low electrode potential of Li/Na-intercalated tetr- and tri-Mo2B2 is beneficial to their performance as anode materials. This work is of great importance for widening the families of both anode materials for LIBs/NIBs and two-dimensional transition metal borides.

An improved protein structure evaluation using a semi-empirically derived structure property

BackgroundIn the backdrop of challenge to obtain a protein structure under the known limitations of both experimental and theoretical techniques, the need of a fast as well as accurate protein structure evaluation method still exists to substantially reduce a huge gap between number of known sequences and structures. Among currently practiced theoretical techniques, homology modelling backed by molecular dynamics based optimization appears to be the most popular one. However it suffers from contradictory indications of different validation parameters generated from a set of protein models which are predicted against a particular target protein. For example, in one model Ramachandran Score may be quite high making it acceptable, whereas, its potential energy may not be very low making it unacceptable and vice versa. Towards resolving this problem, the main objective of this study was fixed as to utilize a simple experimentally derived output, Surface Roughness Index of concerned protein of unknown structure as an intervening agent that could be obtained using ordinary microscopic images of heat denatured aggregates of the same protein.ResultIt was intriguing to observe that direct experimental knowledge of the concerned protein, however simple it may be, might give insight on acceptability of its particular structural model out of a confusion set of models generated from database driven comparative technique for structure prediction. The result obtained from a widely varying structural class of proteins indicated that speed of protein structure evaluation can be further enhanced without compromising with accuracy by recruiting simple experimental output.ConclusionIn this work, a semi-empirical methodological approach was provided for improving protein structure evaluation. It showed that, once structure models of a protein were obtained through homology technique, the problem of selection of a best model out of a confusion set of Pareto-optimal structures could be resolved by employing a structure agent directly obtainable through experiment with the same protein as experimental ingredient. Overall, in the backdrop of getting a reasonably accurate protein structure of pathogens causing epidemics or biological warfare, such approach could be of use as a plausible solution for fast drug design.

Accurate prediction of protein contact maps by coupling residual two-dimensional bidirectional long short-term memory with convolutional neural networks.

Accurate prediction of a protein contact map depends greatly on capturing as much contextual information as possible from surrounding residues for a target residue pair. Recently, ultra-deep residual convolutional networks were found to be state-of-the-art in the latest Critical Assessment of Structure Prediction techniques (CASP12) for protein contact map prediction by attempting to provide a protein-wide context at each residue pair. Recurrent neural networks have seen great success in recent protein residue classification problems due to their ability to propagate information through long protein sequences, especially Long Short-Term Memory (LSTM) cells. Here, we propose a novel protein contact map prediction method by stacking residual convolutional networks with two-dimensional residual bidirectional recurrent LSTM networks, and using both one-dimensional sequence-based and two-dimensional evolutionary coupling-based information. We show that the proposed method achieves a robust performance over validation and independent test sets with the Area Under the receiver operating characteristic Curve (AUC) > 0.95 in all tests. When compared to several state-of-the-art methods for independent testing of 228 proteins, the method yields an AUC value of 0.958, whereas the next-best method obtains an AUC of 0.909. More importantly, the improvement is over contacts at all sequence-position separations. Specifically, a 8.95%, 5.65% and 2.84% increase in precision were observed for the top L∕10 predictions over the next best for short, medium and long-range contacts, respectively. This confirms the usefulness of ResNets to congregate the short-range relations and 2D-BRLSTM to propagate the long-range dependencies throughout the entire protein contact map 'image'. SPOT-Contact server url: http://sparks-lab.org/jack/server/SPOT-Contact/. Supplementary data are available at Bioinformatics online.

Use of the UNRES force field in template-assisted prediction of protein structures and the refinement of server models: Test with CASP12 targets

Knowledge-based methods are, at present, the most effective ones for the prediction of protein structures; however, their results heavily depend on the similarity of a target sequence to those of proteins with known structures. On the other hand, the physics-based methods, although still less accurate and more expensive to execute, are independent of databases and give reasonable results where the knowledge-based methods fail because of weak sequence similarity. Therefore, a plausible approach seems to be the use of knowledge-based methods to determine the sections of the structures that correspond to sufficient sequence similarity and physics-based methods to determine the remaining structure. By participating in the 12th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP12) as the KIAS-Gdansk group, we tested our recently developed hybrid approach, in which protein-structure prediction is carried out by using the physics-based UNRES coarse-grained energy function, with restraints derived from the server models. Best predictions among all groups were obtained for 2 targets and 80% of our models were in the upper 50% of the models submitted to CASP. Our method was also able to exclude, with about 70% confidence, the information from the servers that performed poorly on a given target. Moreover, the method resulted in the best models of 2 refinement targets and performed remarkably well on oligomeric targets.

Crystal Structures and Electronic Properties of Xe–Cl Compounds at High Pressure

Crystal structure prediction techniques coupled with enthalpies obtained at 0 K from density functional theory calculations suggest that pressure can be used to stabilize the chlorides of xenon. In particular, XeCl and XeCl2 were calculated to become metastable by 10 GPa and thermodynamically stable with respect to the elemental phases by 60 GPa. Whereas at low pressures Cl2 dimers were found in the stable phases, zigzag Cl chains were present in Cmcm XeCl at 60 GPa and atomistic chlorine comprised P63/mmc XeCl and Fd3̅m XeCl2 at 100 GPa. A XeCl4 phase that was metastable at 100 GPa contained monomers, dimers, and trimers of chlorine. XeCl, XeCl2, and XeCl4 were metallic at 100 GPa, and at this pressure they were predicted to be superconducting below 9.0, 4.3, and 0.3 K, respectively. Spectroscopic properties of the predicted phases are presented to aid in their eventual characterization, should they ever be synthesized.

Hexagonal Ti2B2 monolayer: a promising anode material offering high rate capability for Li-ion and Na-ion batteries.

Combining the first-principles density functional method and crystal structure prediction techniques, we report a series of hexagonal two-dimensional transition metal borides including Sc2B2, Ti2B2, V2B2, Cr2B2, Y2B2, Zr2B2, and Mo2B2. Their dynamic and thermal stabilities are testified by phonon and molecular dynamics simulations. We investigate the potential of the two-dimensional Ti2B2 monolayer as an anode material for Li-ion and Na-ion batteries. The Ti2B2 monolayer possesses high theoretical specific capacities of 456 and 342 mA h g-1 for Li and Na, respectively. The very high Li/Na diffusivity with an ultralow energy barrier of 0.017/0.008 eV indicates an excellent charge-discharge capability. In addition, good electronic conductivity during the whole lithiation process is found by electronic structure calculations. The very small change in volume after the adsorption of one, two, and three layers of Li and Na ions indicates that the Ti2B2 monolayer is robust. These results highlight the suitability of Ti2B2 monolayer as well as the other two-dimensional transition metal borides as excellent anode materials for both Li-ion and Na-ion batteries.

Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12.

Every second year, the community experiment "Critical Assessment of Techniques for Structure Prediction" (CASP) is conducting an independent blind assessment of structure prediction methods, providing a framework for comparing the performance of different approaches and discussing the latest developments in the field. Yet, developers of automated computational modeling methods clearly benefit from more frequent evaluations based on larger sets of data. The "Continuous Automated Model EvaluatiOn (CAMEO)" platform complements the CASP experiment by conducting fully automated blind prediction assessments based on the weekly pre-release of sequences of those structures, which are going to be published in the next release of the PDB Protein Data Bank. CAMEO publishes weekly benchmarking results based on models collected during a 4-day prediction window, on average assessing ca. 100 targets during a time frame of 5 weeks. CAMEO benchmarking data is generated consistently for all participating methods at the same point in time, enabling developers to benchmark and cross-validate their method's performance, and directly refer to the benchmarking results in publications. In order to facilitate server development and promote shorter release cycles, CAMEO sends weekly email with submission statistics and low performance warnings. Many participants of CASP have successfully employed CAMEO when preparing their methods for upcoming community experiments. CAMEO offers a variety of scores to allow benchmarking diverse aspects of structure prediction methods. By introducing new scoring schemes, CAMEO facilitates new development in areas of active research, for example, modeling quaternary structure, complexes, or ligand binding sites.

Template-based quaternary structure prediction of proteins using enhanced profile-profile alignments.

Proteins often exist as their multimeric forms when they function as so‐called biological assemblies consisting of the specific number and arrangement of protein subunits. Consequently, elucidating biological assemblies is necessary to improve understanding of protein function. Template‐Based Modeling (TBM), based on known protein structures, has been used widely for protein structure prediction. Actually, TBM has become an increasingly useful approach in recent years because of the increased amounts of information related to protein amino acid sequences and three‐dimensional structures. An apparently similar situation exists for biological assembly structure prediction as protein complex structures in the PDB increase, although the inference of biological assemblies is not a trivial task. Many methods using TBM, including ours, have been developed for protein structure prediction. Using enhanced profile–profile alignments, we participated in the 12th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP12), as the FONT team (Group # 480). Herein, we present experimental procedures and results of retrospective analyses using our approach for the Quaternary Structure Prediction category of CASP12. We performed profile–profile alignments of several types, based on FORTE, our profile–profile alignment algorithm, to identify suitable templates. Results show that these alignment results enable us to find templates in almost all possible cases. Moreover, we have come to understand the necessity of developing a model selection method that provides improved accuracy. Results also demonstrate that, to some extent, finding templates of protein complexes is useful even for MEDIUM and HARD assembly prediction.

Ab-initio conformational epitope structure prediction using genetic algorithm and SVM for vaccine design

Background and objectiveT-cell epitope structure identification is a significant challenging immunoinformatic problem within epitope-based vaccine design. Epitopes or antigenic peptides are a set of amino acids that bind with the Major Histocompatibility Complex (MHC) molecules. The aim of this process is presented by Antigen Presenting Cells to be inspected by T-cells. MHC-molecule-binding epitopes are responsible for triggering the immune response to antigens. The epitope's three-dimensional (3D) molecular structure (i.e., tertiary structure) reflects its proper function. Therefore, the identification of MHC class-II epitopes structure is a significant step towards epitope-based vaccine design and understanding of the immune system. MethodsIn this paper, we propose a new technique using a Genetic Algorithm for Predicting the Epitope Structure (GAPES), to predict the structure of MHC class-II epitopes based on their sequence. The proposed Elitist-based genetic algorithm for predicting the epitope's tertiary structure is based on Ab-Initio Empirical Conformational Energy Program for Peptides (ECEPP) Force Field Model. The developed secondary structure prediction technique relies on Ramachandran Plot. We used two alignment algorithms: the ROSS alignment and TM-Score alignment. We applied four different alignment approaches to calculate the similarity scores of the dataset under test. We utilized the support vector machine (SVM) classifier as an evaluation of the prediction performance. ResultsThe prediction accuracy and the Area Under Receiver Operating Characteristic (ROC) Curve (AUC) were calculated as measures of performance. The calculations are performed on twelve similarity-reduced datasets of the Immune Epitope Data Base (IEDB) and a large dataset of peptide-binding affinities to HLA-DRB1*0101. The results showed that GAPES was reliable and very accurate. We achieved an average prediction accuracy of 93.50% and an average AUC of 0.974 in the IEDB dataset. Also, we achieved an accuracy of 95.125% and an AUC of 0.987 on the HLA-DRB1*0101 allele of the Wang benchmark dataset. ConclusionsThe results indicate that the proposed prediction technique “GAPES” is a promising technique that will help researchers and scientists to predict the protein structure and it will assist them in the intelligent design of new epitope-based vaccines.

Hydrogen‐rich superconductors at high pressures

The hydrogen‐rich superconductors stabilized at high‐pressure conditions have been the subject of topic interests. There is an essential hope that hydrogen‐rich superconductors are promising candidates of room‐temperature superconductors. Recent advances in first‐principles crystal structure prediction techniques have opened up the possibility of reliable prediction of superconductive structures, and subsequent superconductivity calculations based on phonon‐mediated superconducting mechanism revealed a general appearance of high temperature superconductivity in pressurized hydrides. Theory‐orientated experiments at high pressure discovered a number of hydrogen‐rich superconductors, among which sulfur hydrides exhibit a remarkably high superconducting critical temperature reaching 203 K. In this review, we discuss the emerging research activities towards hydrogen‐rich superconductors at high pressures and outlook the future direction in the field. WIREs Comput Mol Sci 2018, 8:e1330. doi: 10.1002/wcms.1330This article is categorized under: Structure and Mechanism > Computational Materials Science

Oxysulfide Lialso: A Lithium Superionic Conductor from First Principles

Searching for lithium superionic conductors attracts a lot of attention over the past few decades, especially for the development of all-solid-state lithium batterie. So far, the only kinds of solid material with ionic conductivity over 10-2 S/cm at room temperature are among sulfide materials (Li10GeP2S12 and its derivative, Li7P3S11 glass-ceramic, and Li9.54Si1.74P1.44S11.7Cl0.3). However, sulfides generally suffer from high sensitive to moisture and poor stability of solid electrolyte/oxide cathode interfaces. Compared with sulfides, the oxide SEs have significantly better chemical and electrochemical stability, whereas the highest ionic conductivity achieved in them is still one order of magnitude lower than those of liquid electrolytes. To combine the advantages of sulfides and oxides, anion mixing is expected to be a promising strategy to design the lithium superionic conductors with improved stabilities and high ionic conductivity. To the best of our knowledge, there is still no discovery of crystalline oxysulfide for SEs used in lithium batteries. In this work for the first time we identify a new layered oxysulfide LiAlSO in orthorhombic structure as a novel lithium superionic conductor through first-principles calculations and crystal structure prediction techniques. Two kinds of stacking sequences of AlS2O2 layer are found in different temperature ranges. Phonon and molecular dynamics simulations verify their dynamic stabilities, and wide band gaps up to 5.6 eV are found by electronic structure calculations. The lithium migration energy barrier simulations reveal the collective interstitial-host ion “kick-off” hopping mode with barriers lower than 50 meV as the dominating conduction mechanism for LiAlSO, making it a promising solid state electrolyte in lithium secondary batteries with fast ionic conductivity and wide electrochemical window. This is a first attempt that the lithium superionic conductors are designed by crystal structure prediction method and may help explore other mixed-anion battery materials. Acknowledgment We acknowledge the National Natural Science Foundation of China (Grant No. 11234013), ‘‘863’’ Project (Grant No. 2015AA034201), and the Beijing S&T Project (Grant No. D161100002416003) for financial support and the Shanghai Supercomputer Center for providing computing resources. We would like to express our thanks to Prof. Jiawang Hong (Beijing Institute of Technology) for his fruitful discussion.

Oxysulfide LiAlSO: A Lithium Superionic Conductor from First Principles.

Through first-principles calculations and crystal structure prediction techniques, we identify a new layered oxysulfide LiAlSO in orthorhombic structure as a novel lithium superionic conductor. Two kinds of stacking sequences of layers of AlS_{2}O_{2} are found in different temperature ranges. Phonon and molecular dynamics simulations verify their dynamic stabilities, and wide band gaps up to 5.6eV are found by electronic structure calculations. The lithium migration energy barrier simulations reveal the collective interstitial-host ion "kick-off" hopping mode with barriers lower than 50meV as the dominating conduction mechanism for LiAlSO, indicating it to be a promising solid-state electrolyte in lithium secondary batteries with fast ionic conductivity and a wide electrochemical window. This is a first attempt in which the lithium superionic conductors are designed by the crystal structure prediction method and may help explore other mixed-anion battery materials.

Large-scale identification of coevolution signals across homo-oligomeric protein interfaces by direct coupling analysis

Proteins have evolved to perform diverse cellular functions, from serving as reaction catalysts to coordinating cellular propagation and development. Frequently, proteins do not exert their full potential as monomers but rather undergo concerted interactions as either homo-oligomers or with other proteins as hetero-oligomers. The experimental study of such protein complexes and interactions has been arduous. Theoretical structure prediction methods are an attractive alternative. Here, we investigate homo-oligomeric interfaces by tracing residue coevolution via the global statistical direct coupling analysis (DCA). DCA can accurately infer spatial adjacencies between residues. These adjacencies can be included as constraints in structure prediction techniques to predict high-resolution models. By taking advantage of the ongoing exponential growth of sequence databases, we go significantly beyond anecdotal cases of a few protein families and apply DCA to a systematic large-scale study of nearly 2,000 Pfam protein families with sufficient sequence information and structurally resolved homo-oligomeric interfaces. We find that large interfaces are commonly identified by DCA. We further demonstrate that DCA can differentiate between subfamilies with different binding modes within one large Pfam family. Sequence-derived contact information for the subfamilies proves sufficient to assemble accurate structural models of the diverse protein-oligomers. Thus, we provide an approach to investigate oligomerization for arbitrary protein families leading to structural models complementary to often-difficult experimental methods. Combined with ever more abundant sequential data, we anticipate that this study will be instrumental to allow the structural description of many heteroprotein complexes in the future.

Prediction of Local Quality of Protein Structure Models Considering Spatial Neighbors in Graphical Models

Protein tertiary structure prediction methods have matured in recent years. However, some proteins defy accurate prediction due to factors such as inadequate template structures. While existing model quality assessment methods predict global model quality relatively well, there is substantial room for improvement in local quality assessment, i.e. assessment of the error at each residue position in a model. Local quality is a very important information for practical applications of structure models such as interpreting/designing site-directed mutagenesis of proteins. We have developed a novel local quality assessment method for protein tertiary structure models. The method, named Graph-based Model Quality assessment method (GMQ), explicitly considers the predicted quality of spatially neighboring residues using a graph representation of a query protein structure model. GMQ uses conditional random field as its core of the algorithm, and performs a binary prediction of the quality of each residue in a model, indicating if a residue position is likely to be within an error cutoff or not. The accuracy of GMQ was improved by considering larger graphs to include quality information of more surrounding residues. Moreover, we found that using different edge weights in graphs reflecting different secondary structures further improves the accuracy. GMQ showed competitive performance on a benchmark for quality assessment of structure models from the Critical Assessment of Techniques for Protein Structure Prediction (CASP).

Superconductivity in Hydrides Doped with Main Group Elements Under Pressure

AbstractA priori crystal structure prediction techniques have been used to explore the phase diagrams of hydrides of main group elements under pressure. A number of novel phases with the chemical formulas MHn, n > 1 and M = Li, Na, K, Rb, Cs; MHn, n > 2 and M= Mg, Ca, Sr, Ba; H

Distributed Machine Learning Algorithms to classify Protein secondary structures for Drug Design-A Survey

The arena of secondary structure based drug design is an evolving area in recent years. Structures help us to understand how the protein functions, and will help to design chemicals to be used pharmaceutically as modifiers of enzyme activity. X-Ray structures usually provide us with a quite static picture of the protein which is considered as expensive and time consuming. The recent modern drug design and development using knowledge of proteomics must rely on computational intelligence based machine learning model structures using efficient structure prediction techniques. In recent years, Machine learning, emerging on the basis of parallel and distributed computing for handling big data, is making huge advances in many areas. In this paper, we have captured a comprehensive review of protein structure prediction methods using Machine Learning approach in Distributed environment. Overall, the results are good and depicts that the accuracy and performance of protein secondary structure prediction methods are achieved using Machine Learning techniques and these would become a powerful aid, while implemented in Distributed environment like Hadoop or Spark. This research will be helpful for the recent medicine researchers, which aids in understanding the relation between protein sequences, structure and thereby determine the function to develop various drugs and designing novel enzymes and this is considered as one of the major focused areas in recent bioinformatics research.

Sixty-five years of the long march in protein secondary structure prediction: the final stretch?

Protein secondary structure prediction began in 1951 when Pauling and Corey predicted helical and sheet conformations for protein polypeptide backbone even before the first protein structure was determined. Sixty-five years later, powerful new methods breathe new life into this field. The highest three-state accuracy without relying on structure templates is now at 82–84%, a number unthinkable just a few years ago. These improvements came from increasingly larger databases of protein sequences and structures for training, the use of template secondary structure information and more powerful deep learning techniques. As we are approaching to the theoretical limit of three-state prediction (88–90%), alternative to secondary structure prediction (prediction of backbone torsion angles and Cα-atom-based angles and torsion angles) not only has more room for further improvement but also allows direct prediction of three-dimensional fragment structures with constantly improved accuracy. About 20% of all 40-residue fragments in a database of 1199 non-redundant proteins have <6 Å root-mean-squared distance from the native conformations by SPIDER2. More powerful deep learning methods with improved capability of capturing long-range interactions begin to emerge as the next generation of techniques for secondary structure prediction. The time has come to finish off the final stretch of the long march towards protein secondary structure prediction.

Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks.

Capturing long-range interactions between structural but not sequence neighbors of proteins is a long-standing challenging problem in bioinformatics. Recently, long short-term memory (LSTM) networks have significantly improved the accuracy of speech and image classification problems by remembering useful past information in long sequential events. Here, we have implemented deep bidirectional LSTM recurrent neural networks in the problem of protein intrinsic disorder prediction. The new method, named SPOT-Disorder, has steadily improved over a similar method using a traditional, window-based neural network (SPINE-D) in all datasets tested without separate training on short and long disordered regions. Independent tests on four other datasets including the datasets from critical assessment of structure prediction (CASP) techniques and >10 000 annotated proteins from MobiDB, confirmed SPOT-Disorder as one of the best methods in disorder prediction. Moreover, initial studies indicate that the method is more accurate in predicting functional sites in disordered regions. These results highlight the usefulness combining LSTM with deep bidirectional recurrent neural networks in capturing non-local, long-range interactions for bioinformatics applications. SPOT-disorder is available as a web server and as a standalone program at: http://sparks-lab.org/server/SPOT-disorder/index.php . j.hanson@griffith.edu.au or yuedong.yang@griffith.edu.au or yaoqi.zhou@griffith.edu.au. Supplementary data is available at Bioinformatics online.

Use of Restraints from Consensus Fragments of Multiple Server Models To Enhance Protein-Structure Prediction Capability of the UNRES Force Field.

Recently, we developed a new approach to protein-structure prediction, which combines template-based modeling with the physics-based coarse-grained UNited RESidue (UNRES) force field. In this approach, restrained multiplexed replica exchange molecular dynamics simulations with UNRES, with the Cα-distance and virtual-bond-dihedral-angle restraints derived from knowledge-based models are carried out. In this work, we report a test of this approach in the 11th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP11), in which we used the template-based models from early-stage predictions by the LEE group CASP11 server (group 038, called "nns"), and further improvement of the method. The quality of the models obtained in CASP11 was better than that resulting from unrestrained UNRES simulations; however, the obtained models were generally worse than the final nns models. Calculations with the final nns models, performed after CASP11, resulted in substantial improvement, especially for multi-domain proteins. Based on these results, we modified the procedure by deriving restraints from models from multiple servers, in this study the four top-performing servers in CASP11 (nns, BAKER-ROSETTASERVER, Zhang-server, and QUARK), and implementing either all restraints or only the restraints on the fragments that appear similar in the majority of models (the consensus fragments), outlier models discarded. Tests with 29 CASP11 human-prediction targets with length less than 400 amino-acid residues demonstrated that the consensus-fragment approach gave better results, i.e., lower α-carbon root-mean-square deviation from the experimental structures, higher template modeling score, and global distance test total score values than the best of the parent server models. Apart from global improvement (repacking and improving the orientation of domains and other substructures), improvement was also reached for template-based modeling targets, indicating that the approach has refinement capacity. Therefore, the consensus-fragment analysis is able to remove lower-quality models and poor-quality parts of the models without knowing the experimental structure.

Predicting Global Minimum in Complex Beryllium Borate System for Deep-ultraviolet Functional Optical Applications.

Searching for high performance materials for optical communication and laser industry in deep-ultraviolet (DUV) region has been the subject of considerable interest. Such materials by design from scratching on multi-component complex crystal systems are challenging. Here, we predict, through density function calculations and unbiased structure searching techniques, the formation of quaternary NaBeBO3 compounds at ambient pressure. Among the four low-energy phases, the P63/m structure exhibits a DUV cutoff edge of 20 nm shorter than α-BaB2O4 (189 nm) – the best-known DUV birefringent material. While the P-6 structure exhibits one time second-harmonic generation efficiency of KH2PO4 and possesses excellent crystal growth habit without showing any layer habit as observed in the only available DUV nonlinear optical material KBe2BO3F2, whose layer habit limits its wide industrial applications. These NaBeBO3 structures are promising candidates for the next generation of DUV optical materials, and the structure prediction technique will shed light on future optical materials design.